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	 f4ab2a4147
			
		
	
	f4ab2a4147
	
	
	
		
			
			* merged the changes from deepseeker models to main branch
* Moved regex patterns to unicode.cpp and updated unicode.h
* Moved header files
* Resolved issues
* added and refactored unicode_regex_split and related functions
* Updated/merged the deepseek coder pr
* Refactored code
* Adding unicode regex mappings
* Adding unicode regex function
* Added needed functionality, testing remains
* Fixed issues
* Fixed issue with gpt2 regex custom preprocessor
* unicode : fix? unicode_wstring_to_utf8
* lint : fix whitespaces
* tests : add tokenizer tests for numbers
* unicode : remove redundant headers
* tests : remove and rename tokenizer test scripts
* tests : add sample usage
* gguf-py : reader prints warnings on duplicate keys
* llama : towards llama3 tokenization support (wip)
* unicode : shot in the dark to fix tests on Windows
* unicode : first try custom implementations
* convert : add "tokenizer.ggml.pre" GGUF KV (wip)
* llama : use new pre-tokenizer type
* convert : fix pre-tokenizer type writing
* lint : fix
* make : add test-tokenizer-0-llama-v3
* wip
* models : add llama v3 vocab file
* llama : adapt punctuation regex + add llama 3 regex
* minor
* unicode : set bomb
* unicode : set bomb
* unicode : always use std::wregex
* unicode : support \p{N}, \p{L} and \p{P} natively
* unicode : try fix windows
* unicode : category support via std::regex
* unicode : clean-up
* unicode : simplify
* convert : add convert-hf-to-gguf-update.py
ggml-ci
* lint : update
* convert : add falcon
ggml-ci
* unicode : normalize signatures
* lint : fix
* lint : fix
* convert : remove unused functions
* convert : add comments
* convert : exercise contractions
ggml-ci
* lint : fix
* cmake : refactor test targets
* tests : refactor vocab tests
ggml-ci
* tests : add more vocabs and tests
ggml-ci
* unicode : cleanup
* scripts : ignore new update script in check-requirements.sh
* models : add phi-3, mpt, gpt-2, starcoder
* tests : disable obsolete
ggml-ci
* tests : use faster bpe test
ggml-ci
* llama : more prominent warning for old BPE models
* tests : disable test-tokenizer-1-bpe due to slowness
ggml-ci
---------
Co-authored-by: Jaggzh <jaggz.h@gmail.com>
Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
		
	
		
			
				
	
	
		
			112 lines
		
	
	
		
			3.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			112 lines
		
	
	
		
			3.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| #include "llama.h"
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| #include "common.h"
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| #include "unicode.h"
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| #include "console.h"
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| 
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| #include <cassert>
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| #include <codecvt>
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| #include <cstdio>
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| #include <cstring>
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| #include <locale>
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| #include <string>
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| #include <thread>
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| #include <vector>
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| 
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| int main(int argc, char ** argv) {
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|     if (argc < 2) {
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|         fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
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|         return 1;
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|     }
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| 
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|     const std::string fname = argv[1];
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| 
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|     fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
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| 
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|     llama_model * model;
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|     llama_context * ctx;
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| 
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|     llama_backend_init();
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| 
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|     // load the vocab
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|     {
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|         auto mparams = llama_model_default_params();
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| 
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|         mparams.vocab_only = true;
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| 
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|         model = llama_load_model_from_file(fname.c_str(), mparams);
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| 
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|         if (model == NULL) {
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|             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
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|             return 1;
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|         }
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| 
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|         auto cparams = llama_context_default_params();
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| 
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|         ctx = llama_new_context_with_model(model, cparams);
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| 
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|         if (ctx == NULL) {
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|             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
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|             llama_free_model(model);
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|             return 1;
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|         }
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|     }
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| 
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|     GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
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| 
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| #ifdef _WIN32
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|     // We need this for unicode console support
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|     console::init(false, false);
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|     atexit([]() { console::cleanup(); });
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| #endif
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| 
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|     const int n_vocab = llama_n_vocab(model);
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| 
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|     for (int i = 0; i < n_vocab; ++i) {
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|         std::string str = llama_detokenize_spm(ctx, std::vector<int>(1, i));
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|         std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
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|         std::string check = llama_detokenize_spm(ctx, tokens);
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|         if (check != str) {
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|             fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n",
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|                 __func__, i, str.c_str(), str.length(), check.c_str(), check.length());
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|             return 2;
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|         }
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|     }
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| 
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|     // unicode
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|     {
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|         const int nthread = std::thread::hardware_concurrency();
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| 
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|         std::vector<std::thread> threads(nthread);
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| 
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|         for (int i = 0; i < nthread; ++i) {
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|             threads[i] = std::thread([i, nthread, ctx]() {
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|                 for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) {
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|                     if (cp >= 0xd800 && cp <= 0xdfff) {
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|                         continue;
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|                     }
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| 
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|                     std::string str = unicode_cpt_to_utf8(cp);
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|                     std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
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|                     std::string check = llama_detokenize_spm(ctx, tokens);
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|                     if (cp != 9601 && str != check) {
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|                         fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
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|                                 cp, check.c_str(), check.length(), str.c_str(), str.length());
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|                         std::exit(3);
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|                     }
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|                 }
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|             });
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|         }
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| 
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|         for (auto & t : threads) {
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|             t.join();
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|         }
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|     }
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| 
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|     llama_free_model(model);
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|     llama_free(ctx);
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| 
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|     llama_backend_free();
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| 
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|     return 0;
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| }
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